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    Data on Machine Learning Detailed by Researchers at Instituto Politecnico de Braganca (Hybrid Approaches To Optimization and Machine Learning Methods: a Systematic Literature Review)

    38-39页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting out of Braganca, Portugal, by NewsRx editors, research stated, “Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies.” Financial support for this research came from Instituto Politcnico de Bragana. Our news journalists obtained a quote from the research from Instituto Politecnico de Braganca, “In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Reports Outline Robotics Findings from Taiyuan University of Technology (Dual-type Marker Fusion-based Underwater Visual Localization for Autonomous Docking)

    39-40页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting out of Taiyuan, People’s Republic of China, by NewsRx editors, research stated, “Underwater localization is necessary for autonomous operation of underwater robots. Limited field of view for onboard vision systems can result in poor reliability of visual localization for underwater robots during autonomous docking.” Funders for this research include National Natural Science Foundation of China (NSFC), Ministry of Education for Equipment Pre-Research. Our news journalists obtained a quote from the research from the Taiyuan University of Technology, “This article proposes a dual-type marker fusion-based visual localization method for underwater autonomous docking. First, a visual localization scheme integrating light sources and ArUco markers is designed according to positioning requirements based on varying fields of view for onboard vision in autonomous docking operations. The arrangements of the light sources and ArUco markers are both analyzed and optimized to ensure the reliability and efficiency of visual localization. Then, an underwater visual localization algorithm with dual marker fusion is proposed for high-accuracy localization with a restricted field of view. Extended underwater experiments are conducted to verify the feasibility and stability of the proposed method across various distances.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Center for Mathematics Reports Findings in Machine Learning (Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data)

    40-41页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Caparica, Portugal, by NewsRx correspondents, research stated, “The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data.” Funders for this research include Fundacao para a Ciencia e a Tecnologia, German Federal Ministry of Education and Research, Deutsche Forschungsgemeinschaft, Universitat Hohenheim. Our news journalists obtained a quote from the research from Center for Mathematics, “Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ⅱ) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Studies from Southern University of Science and Technology (SUSTech) in the Area of Machine Learning Described (Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data)

    41-42页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Shenzhen, People’s Republic of China, by NewsRx correspondents, research stated, “Predicting earthquakes through reasonable methods can significantly reduce the damage caused by secondary disasters such as tsunamis.” Funders for this research include National Natural Science Foundation of China; National Key R&D Program of China; Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology. Our news journalists obtained a quote from the research from Southern University of Science and Technology (SUSTech): “Recently, machine learning (ML) approaches have been employed to predict laboratory earthquakes using stick-slip dynamics data obtained from sheared granular fault experiments. Here, we adopt the combined finite-discrete element method (FDEM) to simulate a two-dimensional sheared granular fault system, from which abundant fault dynamics data (i.e., displacement and velocity) during stick-slip cycles are collected at 2203 “sensor” points densely placed along and inside the gouge. We use the simulated data to train LightGBM (Light Gradient Boosting Machine) models and predict the gougeplate friction coefficient (an indicator of stick-slips and the friction state of the fault). To optimize the data, we build the importance ranking of input features and select those with top feature importance for prediction. We then use the optimized data and their statistics for training and finally reach a LightGBM model with an acceptable prediction accuracy (R2 = 0.94). The SHAP (SHapley Additive exPlanations) values of input features are also calculated to quantify their contributions to the prediction.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Researchers at University of Quebec Montreal Target Robotics (Ethics & Robotics, Embodiment and Vulnerability)

    42-43页
    查看更多>>摘要：Data detailed on Robotics have been presented. According to news originating from Montreal, Canada, by NewsRx editors, the research stated, “Focusing on social robots, this article argues that the form of embodiment or presence in the world of agents, whether natural or artificial, is fundamental to their vulnerability and ability to learn. My goal is to compare two different types of artificial social agents, not on the basis of whether they are more or less ‘social’ or ‘intelligent’, but on that of the different ways in which they are embodied, or made present in the world.” Our news journalists obtained a quote from the research from the University of Quebec Montreal, “One type may be called ‘true robots’. That is, machines that are three dimensional physical objects, with three required characteristics: individuality, environmental manipulation and mobility in physical space. The other type may be defined as ‘analytic agents’, for example ‘bots’ and ‘apps’, which in social contexts can act in the world only when embedded in complex systems that include heterogeneous technologies. These two ways of being in the world are quite different from each other, and also from the way human persons are present.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    New Machine Learning Findings from University of York Described (Forecasting Smes’ Credit Risk In Supply Chain Finance With a Sampling Strategy Based On Machine Learning Techniques)

    43-44页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating in York, United Kingdom, by NewsRx journalists, research stated, “Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors.” Funders for this research include National Natural Science Foundation of China (NSFC), Humanity and Social Science Foundation of Ministry of Education of China, Fundamental Research Funds for the Central Universities, China Postdoctoral Science Foundation.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    University of Montpellier Researcher Describes Advances in Machine Learning (Determining Effective Temporal Windows for Rapeseed Detection Using Sentinel-1 Time Series and Machine Learning Algorithms)

    44-45页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news originating from Montpellier, France, by NewsRx correspondents, research stated, “This study investigates the potential of Sentinel-1 (S1) multi-temporal data for the early-season mapping of the rapeseed crop. Additionally, we explore the effectiveness of limiting the portion of a considered time series to map rapeseed fields.” Financial supporters for this research include French Space Study Center; National Research Institute For Agriculture, Food, And The Environment. Our news journalists obtained a quote from the research from University of Montpellier: “To this end, we conducted a quantitative analysis to assess several temporal windows (periods) spanning different phases of the rapeseed phenological cycle in the following two scenarios relating to the availability or constraints of providing ground samples for different years: (i) involving the same year for both training and the test, assuming the availability of ground samples for each year; and (ⅱ) evaluating the temporal transferability of the classifier, considering the constraints of ground sampling. We employed two different classification methods that are renowned for their high performance in land cover mapping: the widely adopted random forest (RF) approach and a deep learning-based convolutional neural network, specifically the InceptionTime algorithm. To assess the classification outcomes, four evaluation metrics (recall, precision, F1 score, and Kappa) were employed. Using S1 time series data covering the entire rapeseed growth cycle, the tested algorithms achieved F1 scores close to 95% on same-year training and testing, and 92.0% when different years were used, both algorithms demonstrated robust performance. For early rapeseed detection within a two-month window post-sowing, RF and InceptionTime achieved F1 scores of 67.5% and 77.2%, respectively, and 79.8% and 88.9% when extended to six months. However, in the context of temporal transferability, both classifiers exhibited mean F1 scores below 50%. Notably, a 5-month time series, covering key growth stages such as stem elongation, inflorescence emergence, and fruit development, yielded a mean F1 score close to 95% for both algorithms when trained and tested in the same year.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Studies from Zhejiang University Provide New Data on Robotics (Design and analysis of an autonomous warehouse robot system with 6-DOF manipulator)

    45-46页
    查看更多>>摘要：Investigators publish new report on robotics. According to news reporting originating from Zhejiang University by NewsRx correspondents, research stated, “With the increasing need for efficiency and accuracy in warehouse operations, the functions and market demands of automated warehouse robots are constantly increasing.” Our news editors obtained a quote from the research from Zhejiang University: “This study presents the design, simulation, and implementation of a warehouse robot, showcasing effective automation solution. Leveraging the Robot Operating System (ROS) and Gazebo, a robot with a six-degree-of-freedom robotic arm for diverse manipulation tasks and a differential drive base for broad-spectrum navigation was designed. The simulation environment in Gazebo faithfully replicates real-world warehouse conditions, en- abling comprehensive path planning and real-time modifications, powered by move_base. A camera sensor serves as the robot’s safety system, designed to detect moving obstacles and initiate appropriate responses, contributing to the enhancement of warehouse safety standards.”
      原文链接:
    • NETL
    • NSTL

    Southern Medical University Reports Findings in Artificial Intelligence (Three-Dimensional Lumbosacral Reconstruction by An Artificial Intelligence-Based Automated MR Image Segmentation for Selecting the Approach of Percutaneous Endoscopic ...)

    46-47页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Guangdong, People’s Republic of China, by NewsRx correspondents, research stated, “Assessing the 3-dimensional (3D) relationship between critical anatomical structures and the surgical channel can help select percutaneous endoscopic lumbar discectomy (PELD) approaches, especially at the L5/S1 level. However, previous evaluation methods for PELD were mainly assessed using 2-dimensional (2D) medical images, making the understanding of the 3D relationship of lumbosacral structures difficult.” Our news editors obtained a quote from the research from Southern Medical University, “Artificial intelligence based on automated magnetic resonance (MR) image segmentation has the benefit of 3D reconstruction of medical images. We developed and validated an artificial intelligence-based MR image segmentation method for constructing a 3D model of lumbosacral structures for selecting the appropriate approach of percutaneous endoscopic lumbar discectomy at the L5/S1 level. Three-dimensional reconstruction study using artificial intelligence based on MR image segmentation. Spine and radiology center of a university hospital. Fifty MR data samples were used to develop an artificial intelligence algorithm for automatic segmentation. Manual segmentation and labeling of vertebrae bone (L5 and S1 vertebrae bone), disc, lumbosacral nerve, iliac bone, and skin at the L5/S1 level by 3 experts were used as ground truth. Five-fold cross-validation was performed, and quantitative segmentation metrics were used to evaluate the performance of artificial intelligence based on the MR image segmentation method. The comparison analysis of quantitative measurements between the artificial intelligence-derived 3D (AI-3D) models and the ground truth-derived 3D (GT-3D) models was used to validate the feasibility of 3D lumbosacral structures reconstruction and preoperative assessment of PELD approaches. Artificial intelligence-based automated MR image segmentation achieved high mean Dice Scores of 0.921, 0.924, 0.885, 0.808, 0.886, and 0.816 for L5 vertebrae bone, S1 vertebrae bone, disc, lumbosacral nerves, iliac bone, and skin, respectively. There were no significant differences between AI-3D and GT-3D models in quantitative measurements. Comparative analysis of quantitative measures showed a high correlation and consistency. Our method did not involve vessel segmentation in automated MR image segmentation. Our study’s sample size was small, and the findings need to be validated in a prospective study with a large sample size.”
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Findings from University of Salamanca Described (Koopaml: a Graphical Platform for Building Machine Learning Pipelines Adapted To Health Professionals)

    47-48页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news originating from Salamanca, Spain, by NewsRx correspondents, research stated, “Machine Learning (ML) has extended its use in several domains to support complex analyses of data. The medical field, in which significant quantities of data are continuously generated, is one of the domains that can benefit from the application of ML pipelines to solve specific problems such as diagnosis, classification, disease detection, segmentation, assessment of organ functions, etc.” Financial supporters for this research include Spanish Ministry of Education and Vocational Training under an FPU fellowship, Spanish Government Ministry of Economy and Competitiveness through the DEFINES project, Spanish Government, Institute of Health Carlos III, Spanish Ministry of Economy and Competitiveness - (ERDF/ESF, “Investing in your future”), National Institute of Health Carlos III, Spanish Government, ERDF/ESF Investing in your future and community.
      原文链接:
    • NETL
    • NSTL
    • Univ Int Rioja-Unir